Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China
<p>Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulations and obser...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-12-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/20/15207/2020/acp-20-15207-2020.pdf |
| Summary: | <p>Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance.
A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulations and observations.
In practice, emission uncertainties are constructed empirically; hence, a partially unrepresentative covariance is unavoidable.
Concerning its complex parameterization,
dust emissions are a typical example where the uncertainty could be induced from many
underlying inputs, e.g., information on soil composition and moisture, land cover and erosive wind velocity, and these can hardly be taken into account together.
This paper describes how an adjoint model can be used to detect
errors in the emission uncertainty assumptions.
This adjoint-based sensitivity method could serve as a supplement of a data assimilation inverse modeling system to trace back the error sources in case large observation-minus-simulation residues remain after assimilation based on empirical background covariance.</p>
<p>The method follows an application of a data assimilation emission inversion for an extreme severe dust storm over East Asia <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx31">Jin et al.</a>, <a href="#bib1.bibx31">2019</a><a href="#bib1.bibx31">b</a>)</span>.
The assimilation system successfully resolved observation-minus-simulation
errors using satellite AOD observations in most of the dust-affected regions.
However, a large underestimation of dust in Northeast China remained despite the fact that the assimilated measurements indicated severe dust plumes there. An adjoint implementation
of our dust simulation model is then used to detect the most likely source region for these unresolved dust loads.
The backward modeling points to the Horqin desert as the source region, which was indicated as a non-source region by the existing emission scheme.
The reference emission and uncertainty are then reconstructed over
the Horqin desert by assuming higher surface erodibility.
After the emission reconstruction, the emission inversion is performed
again, and the posterior dust simulations and reality are now in much closer harmony. Based on our results, it is advised that emission sources in dust transport models include the Horqin desert as a more active source region.</p> |
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| ISSN: | 1680-7316 1680-7324 |